{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TIDE COCO Example\n",
    "This example downloads some Mask R-CNN results files from [detectron](https://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md) and then evaluates them using the COCO dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grabbing Mask R-CNN results\n",
    "For demonstration purposes, we'll be using Mask R-CNN results (Model ID 36229740), but you'll want to use your own results in practice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results Downloaded!\n"
     ]
    }
   ],
   "source": [
    "# Download the Mask R-CNN results to test on. Only for demonstration purposes.\n",
    "import urllib.request # For downloading the sample Mask R-CNN annotations\n",
    "\n",
    "bbox_file = 'mask_rcnn_bbox.json'\n",
    "mask_file = 'mask_rcnn_mask.json'\n",
    "\n",
    "urllib.request.urlretrieve('https://dl.fbaipublicfiles.com/detectron/35861795/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_1x.yaml.02_31_37.KqyEK4tT/output/test/coco_2014_minival/generalized_rcnn/bbox_coco_2014_minival_results.json', bbox_file)\n",
    "urllib.request.urlretrieve('https://dl.fbaipublicfiles.com/detectron/35861795/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_1x.yaml.02_31_37.KqyEK4tT/output/test/coco_2014_minival/generalized_rcnn/segmentations_coco_2014_minival_results.json', mask_file)\n",
    "\n",
    "print('Results Downloaded!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running TIDE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the TIDE evaluation toolkit\n",
    "from tidecv import TIDE\n",
    "\n",
    "# Import the datasets we want to use\n",
    "import tidecv.datasets as datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the dataset\n",
    "# This will automatically download COCO's 2017 annotations for you. You can specify your own COCO-style dataset too!\n",
    "# See datasets.py for a list of all the supported datasets.\n",
    "gt = datasets.COCO()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the results in COCO format\n",
    "bbox_results = datasets.COCOResult(bbox_file) # These files were downloaded above.\n",
    "mask_results = datasets.COCOResult(mask_file) # Replace them with your own in practice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a TIDE object to use for evaluation\n",
    "tide = TIDE()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run the evaluations on the standard COCO metrics (i.e., a range of AP with IoU thresholds [50:95])\n",
    "tide.evaluate_range(gt, bbox_results, mode=TIDE.BOX ) # Several options are available here, see the functions\n",
    "tide.evaluate_range(gt, mask_results, mode=TIDE.MASK) # evaluate and evaluate_range for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- mask_rcnn_bbox --\n",
      "\n",
      "bbox AP @ [50-95]: 40.01\n",
      "                                         bbox AP @ [50-95]\n",
      "===================================================================================================\n",
      "  Thresh       50       55       60       65       70       75       80       85       90       95  \n",
      "---------------------------------------------------------------------------------------------------\n",
      "    AP      61.80    59.76    57.07    53.72    49.59    43.66    35.87    25.77    11.68     1.19  \n",
      "===================================================================================================\n",
      "\n",
      "                         Main Errors\n",
      "=============================================================\n",
      "  Type      Cls      Loc     Both     Dupe      Bkg     Miss  \n",
      "-------------------------------------------------------------\n",
      "   dAP     3.40     6.65     1.18     0.19     3.96     7.53  \n",
      "=============================================================\n",
      "\n",
      "        Special Error\n",
      "=============================\n",
      "  Type   FalsePos   FalseNeg  \n",
      "-----------------------------\n",
      "   dAP      16.28      15.57  \n",
      "=============================\n",
      "\n",
      "-- mask_rcnn_mask --\n",
      "\n",
      "mask AP @ [50-95]: 35.92\n",
      "                                         mask AP @ [50-95]\n",
      "===================================================================================================\n",
      "  Thresh       50       55       60       65       70       75       80       85       90       95  \n",
      "---------------------------------------------------------------------------------------------------\n",
      "    AP      58.30    55.61    52.48    48.82    44.05    37.95    30.33    20.96     9.54     1.17  \n",
      "===================================================================================================\n",
      "\n",
      "                         Main Errors\n",
      "=============================================================\n",
      "  Type      Cls      Loc     Both     Dupe      Bkg     Miss  \n",
      "-------------------------------------------------------------\n",
      "   dAP     3.05     9.38     0.58     0.32     4.31     7.43  \n",
      "=============================================================\n",
      "\n",
      "        Special Error\n",
      "=============================\n",
      "  Type   FalsePos   FalseNeg  \n",
      "-----------------------------\n",
      "   dAP      15.50      18.03  \n",
      "=============================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Summarize the evaluations run so far in the console\n",
    "tide.summarize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot summaries for the evaluations run so far\n",
    "tide.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
